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- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
-
- import os
- import logging
- import functools
-
- import numpy as np
-
- import torch
- import torch.nn as nn
- import torch._utils
- import torch.nn.functional as F
-
- BN_MOMENTUM = 0.1
- logger = logging.getLogger(__name__)
-
-
- def conv3x3(in_planes, out_planes, stride=1):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
-
-
- class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
- class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
- self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1,
- bias=False)
- self.bn3 = nn.BatchNorm2d(planes * self.expansion,
- momentum=BN_MOMENTUM)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
- class HighResolutionModule(nn.Module):
- def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
- num_channels, fuse_method, multi_scale_output=True):
- super(HighResolutionModule, self).__init__()
- self._check_branches(
- num_branches, blocks, num_blocks, num_inchannels, num_channels)
-
- self.num_inchannels = num_inchannels
- self.fuse_method = fuse_method
- self.num_branches = num_branches
-
- self.multi_scale_output = multi_scale_output
-
- self.branches = self._make_branches(
- num_branches, blocks, num_blocks, num_channels)
- self.fuse_layers = self._make_fuse_layers()
- self.relu = nn.ReLU(False)
-
- def _check_branches(self, num_branches, blocks, num_blocks,
- num_inchannels, num_channels):
- if num_branches != len(num_blocks):
- error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(
- num_branches, len(num_blocks))
- logger.error(error_msg)
- raise ValueError(error_msg)
-
- if num_branches != len(num_channels):
- error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(
- num_branches, len(num_channels))
- logger.error(error_msg)
- raise ValueError(error_msg)
-
- if num_branches != len(num_inchannels):
- error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(
- num_branches, len(num_inchannels))
- logger.error(error_msg)
- raise ValueError(error_msg)
-
- def _make_one_branch(self, branch_index, block, num_blocks, num_channels,
- stride=1):
- downsample = None
- if stride != 1 or \
- self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.num_inchannels[branch_index],
- num_channels[branch_index] * block.expansion,
- kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(num_channels[branch_index] * block.expansion,
- momentum=BN_MOMENTUM),
- )
-
- layers = []
- layers.append(block(self.num_inchannels[branch_index],
- num_channels[branch_index], stride, downsample))
- self.num_inchannels[branch_index] = \
- num_channels[branch_index] * block.expansion
- for i in range(1, num_blocks[branch_index]):
- layers.append(block(self.num_inchannels[branch_index],
- num_channels[branch_index]))
-
- return nn.Sequential(*layers)
-
- def _make_branches(self, num_branches, block, num_blocks, num_channels):
- branches = []
-
- for i in range(num_branches):
- branches.append(
- self._make_one_branch(i, block, num_blocks, num_channels))
-
- return nn.ModuleList(branches)
-
- def _make_fuse_layers(self):
- if self.num_branches == 1:
- return None
-
- num_branches = self.num_branches
- num_inchannels = self.num_inchannels
- fuse_layers = []
- for i in range(num_branches if self.multi_scale_output else 1):
- fuse_layer = []
- for j in range(num_branches):
- if j > i:
- fuse_layer.append(nn.Sequential(
- nn.Conv2d(num_inchannels[j],
- num_inchannels[i],
- 1,
- 1,
- 0,
- bias=False),
- nn.BatchNorm2d(num_inchannels[i],
- momentum=BN_MOMENTUM),
- nn.Upsample(scale_factor=2**(j-i), mode='nearest')))
- elif j == i:
- fuse_layer.append(None)
- else:
- conv3x3s = []
- for k in range(i-j):
- if k == i - j - 1:
- num_outchannels_conv3x3 = num_inchannels[i]
- conv3x3s.append(nn.Sequential(
- nn.Conv2d(num_inchannels[j],
- num_outchannels_conv3x3,
- 3, 2, 1, bias=False),
- nn.BatchNorm2d(num_outchannels_conv3x3,
- momentum=BN_MOMENTUM)))
- else:
- num_outchannels_conv3x3 = num_inchannels[j]
- conv3x3s.append(nn.Sequential(
- nn.Conv2d(num_inchannels[j],
- num_outchannels_conv3x3,
- 3, 2, 1, bias=False),
- nn.BatchNorm2d(num_outchannels_conv3x3,
- momentum=BN_MOMENTUM),
- nn.ReLU(False)))
- fuse_layer.append(nn.Sequential(*conv3x3s))
- fuse_layers.append(nn.ModuleList(fuse_layer))
-
- return nn.ModuleList(fuse_layers)
-
- def get_num_inchannels(self):
- return self.num_inchannels
-
- def forward(self, x):
- if self.num_branches == 1:
- return [self.branches[0](x[0])]
-
- for i in range(self.num_branches):
- x[i] = self.branches[i](x[i])
-
- x_fuse = []
- for i in range(len(self.fuse_layers)):
- y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
- for j in range(1, self.num_branches):
- if i == j:
- y = y + x[j]
- else:
- y = y + self.fuse_layers[i][j](x[j])
- x_fuse.append(self.relu(y))
-
- return x_fuse
-
-
- blocks_dict = {
- 'BASIC': BasicBlock,
- 'BOTTLENECK': Bottleneck
- }
-
-
- class HighResolutionNet(nn.Module):
-
- def __init__(self, cfg, **kwargs):
- super(HighResolutionNet, self).__init__()
-
- self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,
- bias=False)
- self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
- self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1,
- bias=False)
- self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
- self.relu = nn.ReLU(inplace=True)
-
- self.stage1_cfg = cfg['MODEL']['EXTRA']['STAGE1']
- num_channels = self.stage1_cfg['NUM_CHANNELS'][0]
- block = blocks_dict[self.stage1_cfg['BLOCK']]
- num_blocks = self.stage1_cfg['NUM_BLOCKS'][0]
- self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
- stage1_out_channel = block.expansion*num_channels
-
- self.stage2_cfg = cfg['MODEL']['EXTRA']['STAGE2']
- num_channels = self.stage2_cfg['NUM_CHANNELS']
- block = blocks_dict[self.stage2_cfg['BLOCK']]
- num_channels = [
- num_channels[i] * block.expansion for i in range(len(num_channels))]
- self.transition1 = self._make_transition_layer(
- [stage1_out_channel], num_channels)
- self.stage2, pre_stage_channels = self._make_stage(
- self.stage2_cfg, num_channels)
-
- self.stage3_cfg = cfg['MODEL']['EXTRA']['STAGE3']
- num_channels = self.stage3_cfg['NUM_CHANNELS']
- block = blocks_dict[self.stage3_cfg['BLOCK']]
- num_channels = [
- num_channels[i] * block.expansion for i in range(len(num_channels))]
- self.transition2 = self._make_transition_layer(
- pre_stage_channels, num_channels)
- self.stage3, pre_stage_channels = self._make_stage(
- self.stage3_cfg, num_channels)
-
- self.stage4_cfg = cfg['MODEL']['EXTRA']['STAGE4']
- num_channels = self.stage4_cfg['NUM_CHANNELS']
- block = blocks_dict[self.stage4_cfg['BLOCK']]
- num_channels = [
- num_channels[i] * block.expansion for i in range(len(num_channels))]
- self.transition3 = self._make_transition_layer(
- pre_stage_channels, num_channels)
- self.stage4, pre_stage_channels = self._make_stage(
- self.stage4_cfg, num_channels, multi_scale_output=True)
-
- # Classification Head
- self.incre_modules, self.downsamp_modules, \
- self.final_layer = self._make_head(pre_stage_channels)
-
- self.classifier = nn.Linear(2048, 1000)
-
- def _make_head(self, pre_stage_channels):
- head_block = Bottleneck
- head_channels = [32, 64, 128, 256]
-
- # Increasing the #channels on each resolution
- # from C, 2C, 4C, 8C to 128, 256, 512, 1024
- incre_modules = []
- for i, channels in enumerate(pre_stage_channels):
- incre_module = self._make_layer(head_block,
- channels,
- head_channels[i],
- 1,
- stride=1)
- incre_modules.append(incre_module)
- incre_modules = nn.ModuleList(incre_modules)
-
- # downsampling modules
- downsamp_modules = []
- for i in range(len(pre_stage_channels)-1):
- in_channels = head_channels[i] * head_block.expansion
- out_channels = head_channels[i+1] * head_block.expansion
-
- downsamp_module = nn.Sequential(
- nn.Conv2d(in_channels=in_channels,
- out_channels=out_channels,
- kernel_size=3,
- stride=2,
- padding=1),
- nn.BatchNorm2d(out_channels, momentum=BN_MOMENTUM),
- nn.ReLU(inplace=True)
- )
-
- downsamp_modules.append(downsamp_module)
- downsamp_modules = nn.ModuleList(downsamp_modules)
-
- final_layer = nn.Sequential(
- nn.Conv2d(
- in_channels=head_channels[3] * head_block.expansion,
- out_channels=2048,
- kernel_size=1,
- stride=1,
- padding=0
- ),
- nn.BatchNorm2d(2048, momentum=BN_MOMENTUM),
- nn.ReLU(inplace=True)
- )
-
- return incre_modules, downsamp_modules, final_layer
-
- def _make_transition_layer(
- self, num_channels_pre_layer, num_channels_cur_layer):
- num_branches_cur = len(num_channels_cur_layer)
- num_branches_pre = len(num_channels_pre_layer)
-
- transition_layers = []
- for i in range(num_branches_cur):
- if i < num_branches_pre:
- if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
- transition_layers.append(nn.Sequential(
- nn.Conv2d(num_channels_pre_layer[i],
- num_channels_cur_layer[i],
- 3,
- 1,
- 1,
- bias=False),
- nn.BatchNorm2d(
- num_channels_cur_layer[i], momentum=BN_MOMENTUM),
- nn.ReLU(inplace=True)))
- else:
- transition_layers.append(None)
- else:
- conv3x3s = []
- for j in range(i+1-num_branches_pre):
- inchannels = num_channels_pre_layer[-1]
- outchannels = num_channels_cur_layer[i] \
- if j == i-num_branches_pre else inchannels
- conv3x3s.append(nn.Sequential(
- nn.Conv2d(
- inchannels, outchannels, 3, 2, 1, bias=False),
- nn.BatchNorm2d(outchannels, momentum=BN_MOMENTUM),
- nn.ReLU(inplace=True)))
- transition_layers.append(nn.Sequential(*conv3x3s))
-
- return nn.ModuleList(transition_layers)
-
- def _make_layer(self, block, inplanes, planes, blocks, stride=1):
- downsample = None
- if stride != 1 or inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
- )
-
- layers = []
- layers.append(block(inplanes, planes, stride, downsample))
- inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def _make_stage(self, layer_config, num_inchannels,
- multi_scale_output=True):
- num_modules = layer_config['NUM_MODULES']
- num_branches = layer_config['NUM_BRANCHES']
- num_blocks = layer_config['NUM_BLOCKS']
- num_channels = layer_config['NUM_CHANNELS']
- block = blocks_dict[layer_config['BLOCK']]
- fuse_method = layer_config['FUSE_METHOD']
-
- modules = []
- for i in range(num_modules):
- # multi_scale_output is only used last module
- if not multi_scale_output and i == num_modules - 1:
- reset_multi_scale_output = False
- else:
- reset_multi_scale_output = True
-
- modules.append(
- HighResolutionModule(num_branches,
- block,
- num_blocks,
- num_inchannels,
- num_channels,
- fuse_method,
- reset_multi_scale_output)
- )
- num_inchannels = modules[-1].get_num_inchannels()
-
- return nn.Sequential(*modules), num_inchannels
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.conv2(x)
- x = self.bn2(x)
- x = self.relu(x)
- x = self.layer1(x)
-
- x_list = []
- for i in range(self.stage2_cfg['NUM_BRANCHES']):
- if self.transition1[i] is not None:
- x_list.append(self.transition1[i](x))
- else:
- x_list.append(x)
- y_list = self.stage2(x_list)
-
- x_list = []
- for i in range(self.stage3_cfg['NUM_BRANCHES']):
- if self.transition2[i] is not None:
- x_list.append(self.transition2[i](y_list[-1]))
- else:
- x_list.append(y_list[i])
- y_list = self.stage3(x_list)
-
- x_list = []
- for i in range(self.stage4_cfg['NUM_BRANCHES']):
- if self.transition3[i] is not None:
- x_list.append(self.transition3[i](y_list[-1]))
- else:
- x_list.append(y_list[i])
- y_list = self.stage4(x_list)
-
- # Classification Head
- y = self.incre_modules[0](y_list[0])
- for i in range(len(self.downsamp_modules)):
- y = self.incre_modules[i+1](y_list[i+1]) + \
- self.downsamp_modules[i](y)
-
- y = self.final_layer(y)
-
- if torch._C._get_tracing_state():
- y = y.flatten(start_dim=2).mean(dim=2)
- else:
- y = F.avg_pool2d(y, kernel_size=y.size()
- [2:]).view(y.size(0), -1)
-
- y = self.classifier(y)
-
- return y
-
- def init_weights(self, pretrained='',):
- logger.info('=> init weights from normal distribution')
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(
- m.weight, mode='fan_out', nonlinearity='relu')
- elif isinstance(m, nn.BatchNorm2d):
- nn.init.constant_(m.weight, 1)
- nn.init.constant_(m.bias, 0)
- if os.path.isfile(pretrained):
- pretrained_dict = torch.load(pretrained)
- logger.info('=> loading pretrained model {}'.format(pretrained))
- model_dict = self.state_dict()
- pretrained_dict = {k: v for k, v in pretrained_dict.items()
- if k in model_dict.keys()}
- for k, _ in pretrained_dict.items():
- logger.info(
- '=> loading {} pretrained model {}'.format(k, pretrained))
- model_dict.update(pretrained_dict)
- self.load_state_dict(model_dict)
-
-
- def get_cls_net(config, **kwargs):
- model = HighResolutionNet(config, **kwargs)
- model.init_weights()
- return model
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